Academic literature on the topic 'MR-guided radiotherapy'
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Journal articles on the topic "MR-guided radiotherapy"
Slotman, B., and C. Gani. "Online MR-guided radiotherapy – A new era in radiotherapy." Clinical and Translational Radiation Oncology 18 (September 2019): 102–3. http://dx.doi.org/10.1016/j.ctro.2019.04.011.
Full textJonsson, J. "SP-0006: Challenges in MR guided radiotherapy." Radiotherapy and Oncology 119 (April 2016): S2—S3. http://dx.doi.org/10.1016/s0167-8140(16)31255-5.
Full textvan den Berg, Cornelius. "[I186] New technologies for MR guided radiotherapy." Physica Medica 52 (August 2018): 71. http://dx.doi.org/10.1016/j.ejmp.2018.06.258.
Full textPollard, Julianne M., Zhifei Wen, Ramaswamy Sadagopan, Jihong Wang, and Geoffrey S. Ibbott. "The future of image-guided radiotherapy will be MR guided." British Journal of Radiology 90, no. 1073 (May 2017): 20160667. http://dx.doi.org/10.1259/bjr.20160667.
Full textPalacios, M. A., O. Bohoudi, S. Senan, B. Slotman, A. Bruynzeel, and F. J. Lagerwaard. "1. MR-guided adaptive stereotactic radiotherapy: A new paradigm in radiotherapy." Physica Medica 44 (December 2017): 1. http://dx.doi.org/10.1016/j.ejmp.2017.10.026.
Full textJaffray, D. "SP-0395: Challenges associated with MR guided radiotherapy." Radiotherapy and Oncology 123 (May 2017): S211. http://dx.doi.org/10.1016/s0167-8140(17)30837-x.
Full textRodriguez, Lori L., Rupesh Kotecha, Martin C. Tom, Michael D. Chuong, Jessika A. Contreras, Tino Romaguera, Diane Alvarez, et al. "CT-guided versus MR-guided radiotherapy: Impact on gastrointestinal sparing in adrenal stereotactic body radiotherapy." Radiotherapy and Oncology 166 (January 2022): 101–9. http://dx.doi.org/10.1016/j.radonc.2021.11.024.
Full textSong, Yajun, Zhenjiang Li, Huadong Wang, Yun Zhang, and Jinbo Yue. "MR-LINAC-Guided Adaptive Radiotherapy for Gastric MALT: Two Case Reports and a Literature Review." Radiation 2, no. 3 (July 13, 2022): 259–67. http://dx.doi.org/10.3390/radiation2030019.
Full textFahad, H. M., S. Dorsch, M. Zaiß, and C. P. Karger. "PO-1638 Multiparametric optimization of MR imaging sequences for MR guided radiotherapy." Radiotherapy and Oncology 170 (May 2022): S1433—S1434. http://dx.doi.org/10.1016/s0167-8140(22)03602-7.
Full textSchumacher, Leif-Erik D., Alan Dal Pra, Sarah E. Hoffe, and Eric A. Mellon. "Toxicity reduction required for MRI-guided radiotherapy to be cost-effective in the treatment of localized prostate cancer." British Journal of Radiology 93, no. 1114 (October 1, 2020): 20200028. http://dx.doi.org/10.1259/bjr.20200028.
Full textDissertations / Theses on the topic "MR-guided radiotherapy"
Schellhammer, Sonja. "Technical Feasibility of MR-Integrated Proton Therapy: Beam Deflection and Image Quality." 2019. https://tud.qucosa.de/id/qucosa%3A34132.
Full textThe integration of magnetic resonance imaging (MRI) into proton therapy is expected to strongly increase the targeting accuracy in radiation therapy for cancerous diseases. Especially for tumours situated in mobile organs in the thorax and abdomen, MR-integrated proton therapy (MRiPT) could enable the synchronisation of irradiation to the tumour position, resulting in less dose to normal tissue and reduced side effects. However, such an integration has been hindered so far by a lack of scientific studies on the potential mutual interference between the two components. This thesis was dedicated to two of these sources of interference, namely the deflection of the proton beam by the magnetic field of the MR scanner and, vice versa, alterations of the MR image induced by the electromagnetic fields of the proton therapy facility and by the beam itself. Although previous work has indicated that there is general consensus that the trajectory of a slowing down proton beam in a homogeneous phantom inside a transverse magnetic field is predictable, a quantitative comparison of the published methods, as presented in the first part of this thesis, has shown that predictions of different models only agree for certain proton beam energies and magnetic flux densities. Therefore, shortcomings of previously published analytical methods have been analysed and quantified. The inclusion of critical assumptions and the lack of applicability to realistic, i.e. non-uniform, magnetic flux densities and patient anatomies have been identified as main problems. To overcome these deficiencies, a new semi-analytical model called RAMDIM has been developed. It was shown that this model is both applicable to more realistic setups and less assumptive than existing analytical approaches, and faster than Monte Carlo based particle tracking simulations. This model is expected to be useful in MRiPT for fast and accurate deflection estimations, treatment plan optimisation, and MR-guided beam tracking. In a second step, the magnetic field-induced proton beam deflection has been measured for the first time in a tissue-mimicking medium by film dosimetry and has been compared against Monte Carlo simulations. In a transverse magnetic field of 0.95 T, it was experimentally shown that the lateral Bragg peak displacement ranges between 1 mm and 10 mm for proton energies between 80 and 180 MeV in PMMA. Range retraction was found to be ≤ 0.5 mm. The measured Bragg peak displacement was shown to agree within 0.8 mm with Monte Carlo simulations. These results indicate that proton beam deflection in a homogeneous medium is accurately predictable for intermediate proton beam energies and magnetic flux densities by Monte Carlo simulations and therefore not impeding the feasibility of MRiPT. In the second part of this thesis, an MR scanner has been integrated into a proton beam line for the first time. For this purpose, an open low-field MR scanner has been placed at the end of a fixed horizontal proton research beam line in a proton therapy facility. The beam deflection induced by the static magnetic field of the scanner was taken into account for alignment of the beam and the FOV of the scanner. The pulse sequence-dependent dynamic gradient fields did not measurably affect the transverse beam profile behind the MR scanner. The MR magnetic field homogeneity was within the vendor’s specifications and not relevantly influenced by the rotation of the proton gantry in the neighbouring treatment room. No magnetic field compensation system was required for simultaneous operation of the MR scanner and the proton therapy system. These results proof that simultaneous irradiation and imaging is feasible in an in-beam MR setup. The MR image quality of the in-beam MR scanner was then quantified by an adapted standard protocol comprising spin and gradient echo imaging and shown to be acceptable both with and without simultaneous proton beam irradiation. All geometrical parameters agreed with the mechanical dimensions of the used phantom within one pixel width. As common for low-field MR scanners, the signal-to-noise ratio (SNR) of the MR images was low, which resulted in a low image uniformity and a high ghosting ratio in comparison to the standardised test criteria. Furthermore, a strong fluctuation of the vertical phantom position due to uncertainties in the pre-scan frequency calibration was observed, with an interquartile range of up to 1.5 mm. T2*-weighted gradient echo images showed relevant nonuniform deformations due to magnetic field inhomogeneities. Most image quality parameters were shown to be equivalent with and without simultaneous proton beam irradiation. However, a significant influence of simultaneous irradiation was observed as a shift of the vertical phantom position and a decrease in the SNR, both of which can be explained by a change in the B0 field of the MR scanner induced by components of the fringe field of the beam line magnets directed parallel to B0 . While the decrease in SNR is not expected to be relevant (median differences were within 1.5 ), the sequence-dependent phantom shift (median differences of up to 0.7 mm) can become non-negligible. These results show that the MR images are not severely distorted by simultaneous irradiation, but a dedicated optimisation of the pre-scan RF calibration and the MR sequences is required for MRiPT. Lastly, a current-dependent influence of the proton beam on the MR image was shown to be measurable in water in two different MR sequences, which allowed for range verification measurements. The effect was observed in different liquids but not in highly viscose and solid materials, and most probably induced by heat convection. This method is expected to be useful in MRiPT for consistency tests of the proton range during machine-specific quality assurance. In conclusion, this work has improved and quantified the accuracy of beam deflection predictions and shown the feasibility and potential of in-beam MR imaging, justifying further research towards a first MRiPT prototype.:List of Figures v List of Tables vii 1 General Introduction 1 2 State of the Art: Proton Therapy and Magnetic Resonance Imaging 3 2.1 Proton Therapy 4 2.1.1 Physical Principle 4 2.1.2 Beam Delivery 7 2.1.3 Motion Management and the Role of Image Guidance 10 2.2 Magnetic Resonance Imaging 14 2.2.1 Physical Principle 14 2.2.2 Image Generation by Pulse Sequences 18 2.2.3 Image Quality 21 2.3 MR-Guided Radiotherapy 24 2.3.1 Offline MR Guidance 24 2.3.2 On-line MR Guidance 25 2.4 MR-Integrated Proton Therapy 28 2.4.1 Aims of this Thesis 32 3 Magnetic Field-Induced Beam Deflection and Bragg Peak Displacement 35 3.1 Analytical Description 36 3.1.1 Review of Analytical Models 36 3.1.2 New Model Formulation 41 3.1.3 Evaluation of Analytical and Numerical Models 44 3.1.4 Discussion 51 3.2 Monte Carlo Simulation and Experimental Verification 54 3.2.1 Verification Setup 54 3.2.2 Monte Carlo Simulation 56 3.2.3 Experimental Verification 60 3.2.4 Discussion 61 3.3 Summary 63 4 Integrated In-Beam MR System: Proof of Concept 65 4.1 Integration of a Low-Field MR Scanner and a Static Research Beamline 65 4.1.1 Proton Therapy System 66 4.1.2 MR Scanner 66 4.1.3 Potential Sources of Interference 67 4.1.4 Integration of Both Systems 68 4.2 Beam and Image Quality in the Integrated Setup 70 4.2.1 Beam Profile 70 4.2.2 MR Magnetic Field Homogeneity 72 4.2.3 MR Image Quality - Qualitative In Vivo and Ex Vivo Test 74 4.2.4 MR Image Quality - Quantitative Phantom Tests 77 4.3 Feasibility of MRI-based Range Verification 86 4.3.1 MR Sequences 86 4.3.2 Proton Beam Parameters 88 4.3.3 Target Material Dependence 91 4.3.4 Discussion 92 4.4 Summary 96 5 Discussion and Future Perspectives 99 6 Summary/Zusammenfassung 105 6.1 Summary 105 6.2 Zusammenfassung 108 Bibliography I Supplementary Information XXIX A Beam Deflection: Experimental Measurements XXIX A.1 Setup XXIX A.2 Film Handling and Evaluation XXX A.3 Uncertainty Estimation XXX B Beam Deflection: Monte Carlo Simulations XXXIII B.1 Magnetic Field Model XXXIII B.2 Uncertainty Estimation XXXIV C Integrated MRiPT Setup XXXVI C.1 Magnetic Field Map XXXVI C.2 Sequence Parameters XXXVI C.3 Image Quality Parameters XLII C.4 Range Verification Sequences XLII
Books on the topic "MR-guided radiotherapy"
Kerkmeijer, Linda G. W., Clifton D. Fuller, Ben Slotman, and Vincenzo Valentini, eds. Online Adaptive MR-guided Radiotherapy. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-503-9.
Full textBook chapters on the topic "MR-guided radiotherapy"
Lacornerie, Thomas, Albert Lisbona, and Andrew W. Beavis. "CyberKnife, TomoTherapy and MR-Guided Linear Accelerators." In Handbook of Radiotherapy Physics, Vol1:281—Vol1:294. 2nd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780429201493-17.
Full textVázquez Romaguera, Liset, Tal Mezheritsky, and Samuel Kadoury. "Personalized Respiratory Motion Model Using Conditional Generative Networks for MR-Guided Radiotherapy." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 238–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87202-1_23.
Full textLu, Chao, Sudhakar Chelikani, and James S. Duncan. "A Unified Framework for Joint Segmentation, Nonrigid Registration and Tumor Detection: Application to MR-Guided Radiotherapy." In Lecture Notes in Computer Science, 525–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22092-0_43.
Full textNeph, Ryan, Yangsibo Huang, Youming Yang, and Ke Sheng. "DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy." In Artificial Intelligence in Radiation Therapy, 137–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32486-5_17.
Full textPen, Olga, Borna Maraghechi, Lauren Henke, and Olga Green. "MR-Integrated Linear Accelerators: First Clinical Results." In Image-Guided High-Precision Radiotherapy, 159–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08601-4_7.
Full textMorris, Eric D., Dylan P. O’Connell, Yu Gao, and Minsong Cao. "MR safety considerations for MRI-guided radiotherapy." In Advances in Magnetic Resonance Technology and Applications, 81–100. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-91689-9.00005-4.
Full textLavender, Frances. "Imaging for treatment delivery: Image-guided radiotherapy." In Physics for Clinical Oncology, 163—C10.F5. 2nd ed. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/med/9780198862864.003.0010.
Full textCamilleri, Philip, Andy Gaya, Veni Ezhil, and James Good. "Patient reported outcomes in the use of MR-guided radiotherapy." In Advances in Magnetic Resonance Technology and Applications, 483–90. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-91689-9.00023-6.
Full textGani, Cihan, Luca Boldrini, Vincenzo Valentini, and Daniel Zips. "Online MR-guided radiotherapy in rectal cancer—Dose escalation and beyond." In Advances in Magnetic Resonance Technology and Applications, 367–73. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-91689-9.00018-2.
Full textMichalet, Morgan, Simon Valdenaire, Karl Bordeau, David Azria, and Olivier Riou. "MR-guided radiotherapy for liver tumors: Hepatocarcinomas, cholangiocarcinomas, and liver metastases." In Advances in Magnetic Resonance Technology and Applications, 295–314. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-91689-9.00015-7.
Full textConference papers on the topic "MR-guided radiotherapy"
Hughes, Sophie, Simone Lanfredini, Asmita Thappa, Somnath Mukherjee, and Eric O’Neill. "Abstract B69: Assessment of CCR5/maraviroc immunotherapy in combination with PD1 and MR-guided radiotherapy for treatment of pancreatic cancer." In Abstracts: AACR Special Conference on Tumor Immunology and Immunotherapy; November 17-20, 2019; Boston, MA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/2326-6074.tumimm19-b69.
Full textLanfredini, Simone, Sophie Hughes, Asmita Thapa, Fiona Bangs, Jennifer Morton, Danny Allen, Veerle Kersemans, et al. "Abstract B30: Assessment of CCR5i/maraviroc immunotherapy in combination with PD1 and MR-guided radiotherapy for treatment of pancreatic cancer." In Abstracts: AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; September 6-9, 2019; Boston, MA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.panca19-b30.
Full textReports on the topic "MR-guided radiotherapy"
Chen, Lili. MR-Guided Pulsed High-Intensity Focused Ultrasound Enhancement of Gene Therapy Combined With Androgen Deprivation and Radiotherapy for Prostate Cancer Treatment. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada518248.
Full textChen, Lili. MR Guided Pulsed High Intensity Focused Ultrasound Enhancement of Gene Therapy Combined with Androgen Deprivation and Radiotherapy for Prostate Cancer Treatment. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada569443.
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